Experience

Professional

Presidio Components INC. - Programming Internship (Oct 2021 - Apr 2022)

  • Presidio Components is a Hi-Rel Ceramic Capacitor manufactor, focusing on both standard and custom applications of ceramic capacitors in the commercial, military, and aerospace industries. For more information click here.
  • Utilize computer vision systems and simple motion motors to be implemented in capacitor inspection lines. Also extensively worked to clean up and streamline both customer and in-house Excel spreadsheets.
  • Skills utilized/learned: Python (NumPy, Pandas, OpenPyXL, Google Ortools, Applied Motion motor integration, and other packages), Microsoft Excel (Macros), basic engineering machining concepts, Visco Technology Camera Systems.

Academia

COGS 18 - Introduction to Python

  • Become familiar with the fundamentals of Python.
  • Was an Instructional Assistant in another iteration of the class.

COGS 9 - Introduction to Data Science

  • Concepts of data and its role in science introduced, as well as the ideas behind data-mining, text-mining, machine learning, and graph theory.

DSC 10 - Principles of Data Science

  • Develop computational thinking and tools necessary to answer questions that arise from large-scale datasets. Emphasizes an end-to-end approach to data science, further introducing programming techniques in Python that cover data processing, modeling, and analysis.

DSC 20 - Programming and Basic Data Structures for Data Science

  • Provides an understanding of the structures that underlie the programs, algorithms, and languages used in data science by elaborating computational concepts and exposing students to techniques of abstraction. Topics covered include recursion, higher-order functions, function composition, object-oriented programming, interpreters, classes, and simple data structures such as arrays, lists, and linked lists.

COGS 108 - Data Science in Practice

  • Focus on critical skills needed to pursue a data science career using hands-on programming and experimental challenges.

COGS 137 - Practical Data Science in R

  • Learn coding for data analysis using the R programming language. Course then shifts focus on practical and applied skills in asking data-informed questions, data wrangling, data visualization, building statistical learning models, and communicating findings.

COGS 118A - Supervised Machine Learning Algorithms

  • Introduces the mathematical formulations and algorithmic implementations of the core supervised machine learning methods. Topics include regression, nearest neighborhood, decision tree, support vector machine, and ensemble classifiers.

COGS 118B - Introduction to Machine Learning (Unsupervised)

  • A rigorous introduction to machine learning, specifically focusing on unsupervised models. Topics covered include maximum likelihood estimation, Bayesian parameter estimation, clustering, principal component analysis, and some application areas.

MGT 153 - Business Analytics

  • Learn core business analytics concepts and skills including Excel, relational databases and Structured Query Language (SQL), principles of effective data visualizations and interactive data visualization (e.g., Tableau), and data preprocessing and regression analysis using data analytics programming (e.g., Python).

MGT 183 - Financial Investments

  • Examines financial theory and empirical evidence useful for making investment decisions. Topics include portfolio theory, equilibrium models, market efficiency examined for stock securities and fixed income instruments, risk adjusted ROIs for capital investments’ impact on stock prices and free options.